create_missings: Create missing values in a 'data.frame'
In NErler/simvalidator: Validation of Statistical Models via Simulation

create_missings

R Documentation

Create missing values in a `data.frame`

Create missing values in a data.frame

create_missings(
  data,
  idvars,
  prop_mis,
  prop_cc,
  patterns = NULL,
  weights = NULL,
  type = "RIGHT",
  groups = NULL,
  varlvls = NULL
)

`data`	a complete `data.frame`
`prop_mis`	a named vector indicating the proportion of missing values per variable (only variables with proportions > 0 have to be included)
`prop_cc`	the proportion of complete cases
`patterns`	a matrix specifying the missing data patterns to be used; a `1` indicates a variable is completely observed and `0` that it is incomplete; if unspecified, this is created automatically.
`weights`	a matrix of the same dimension as `patterns` that indicates which variables are predictive of missingness in this pattern; this argument allows to define MCAR, MAR or MNAR missingness. If not specified, `weights` is set equal to `patterns`, i.e., MAR with all observed variables being predictors.